Braking energy distribution method and system based on braking noise prediction and electronic equipment

Abstract

The invention discloses a braking energy distribution method and system based on braking noise prediction and electronic equipment. The method comprises the steps that braking noise time sequence data, vehicle speed time sequence data, hydraulic braking torque time sequence data and pedal travel time sequence data of a current vehicle are obtained; inputting the braking noise time sequence data, the hydraulic braking torque time sequence data and the pedal travel time sequence data into a pre-trained braking noise prediction model to obtain a braking noise prediction value of the current vehicle after a preset time step; determining a corresponding braking noise threshold value according to the current working condition and the current driving mode of the current vehicle; and when the braking noise prediction value is larger than the braking noise threshold value, the braking energy distribution proportion is adjusted, so that the energy recovery torque is increased, and the hydraulic braking torque is reduced. According to the method, the braking noise of the vehicle is maintained below the threshold value through dynamic distribution of the braking energy, interference to users in the vehicle is avoided, the driving comfort of the vehicle is improved, and the method can be applied to the technical field of vehicle control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, and in particular to a brake energy distribution method and system based on brake noise prediction and electronic equipment. BACKGROUND

[0002] Automobile brake noise can affect the driving experience of users. High-frequency or continuous brake noise can also distract the driver and increase the risk of accidents. Users may associate brake noise with poor vehicle quality, leading to after-sales disputes and reducing brand trust. In the prior art, materials are improved (such as damping layers and low-noise brake pads) and passive noise reduction measures (such as soundproofing materials) are used to reduce brake noise, increasing the cost of the vehicle and having limited noise reduction effect.

[0003] The above problems need to be solved. SUMMARY

[0004] The present application aims to at least partially solve one of the technical problems in the prior art.

[0005] To this end, one object of the present application is to provide a brake energy distribution method based on brake noise prediction. This method can quickly adjust the ratio of hydraulic brake torque to energy recovery torque when the vehicle brake noise is about to exceed the threshold value, without increasing the cost of hardware and materials, by predicting brake noise and dynamically distributing brake energy, so that the vehicle brake noise is maintained below the threshold value, avoiding interference with users in the vehicle and improving the driving comfort of the vehicle.

[0006] Another object of the present application is to provide a brake energy distribution system based on brake noise prediction.

[0007] In order to achieve the above technical purpose, the technical solution adopted by the present application comprises: On the one hand, the present application provides a brake energy distribution method based on brake noise prediction, comprising the following steps: Obtaining brake noise time series data, vehicle speed time series data, hydraulic brake torque time series data and pedal stroke time series data of the current vehicle; Inputting the brake noise time series data, the hydraulic brake torque time series data and the pedal stroke time series data into a pre-trained brake noise prediction model to obtain a brake noise prediction value of the current vehicle after a preset time step; Determining a corresponding brake noise threshold value according to the current working condition and the current driving mode of the current vehicle; When the brake noise prediction value is greater than the brake noise threshold value, adjusting the brake energy distribution ratio so that the energy recovery torque increases and the hydraulic brake torque decreases.

[0008] Further, in an embodiment of the present application, the brake noise prediction model is trained by the following steps: obtaining historical brake noise time series data, historical vehicle speed time series data, historical hydraulic brake torque time series data and historical pedal stroke time series data of a test vehicle during a brake test; selecting a peak value of the historical brake noise time series data as a true noise label, and taking a segment before the peak value in the historical brake noise time series data as a brake noise time series sample; cutting the historical vehicle speed time series data, the historical hydraulic brake torque time series data and the historical pedal stroke time series data according to a time range of the brake noise time series sample, to obtain a vehicle speed time series sample, a hydraulic brake torque time series sample and a pedal stroke time series sample; constructing a training sample according to the brake noise time series sample, the vehicle speed time series sample, the hydraulic brake torque time series sample and the pedal stroke time series sample, taking the true noise label as a sample label of the training sample, to obtain a training data set; inputting the training data set into a pre-constructed CNN-LSTM hybrid neural network for training, to obtain the trained brake noise prediction model.

[0009] Further, in an embodiment of the present application, the CNN-LSTM hybrid neural network comprises a multi-branch CNN network, a feature fusion layer, an LSTM layer and an output layer, and the inputting of the training data set into the pre-constructed CNN-LSTM hybrid neural network for training to obtain the trained brake noise prediction model specifically comprises: extracting features of the brake noise time series sample, the vehicle speed time series sample, the hydraulic brake torque time series sample and the pedal stroke time series sample respectively through each branch of the multi-branch CNN network, to obtain brake noise features, vehicle speed features, hydraulic brake torque features and pedal stroke features; performing feature fusion on the brake noise features, the vehicle speed features, the hydraulic brake torque features and the pedal stroke features through the feature fusion layer, to obtain a fusion feature vector; inputting the fusion feature vector at consecutive multiple time steps into the LSTM layer for hidden state calculation, to obtain a hidden state vector; mapping the hidden state vector to a brake noise prediction label through the output layer; determining a loss value according to the brake noise prediction label and the true noise label; updating parameters of the CNN-LSTM hybrid neural network according to the loss value through a back propagation algorithm to obtain the trained brake noise prediction model.

[0010] Further, in an embodiment of the present application, the brake noise threshold value corresponding to the current working condition and the current driving mode of the vehicle is determined, which specifically comprises: obtaining a brake noise threshold value mapping table calibrated in advance through real vehicle test; querying the brake noise threshold value mapping table according to the current working condition and the current driving mode to obtain the brake noise threshold value.

[0011] Further, in an embodiment of the present application, the brake energy distribution ratio is adjusted so that the energy recovery torque increases and the hydraulic brake torque decreases, which specifically comprises: determining a brake noise deviation value according to the brake noise prediction value and the brake noise threshold value; determining a corresponding energy recovery torque compensation value according to the brake noise deviation value through a PID control algorithm; controlling the energy recovery torque to increase and the hydraulic brake torque to decrease according to the energy recovery torque compensation value, and making the sum of the energy recovery torque and the hydraulic brake torque after redistribution meet the brake demand of the vehicle.

[0012] Further, in an embodiment of the present application, the brake energy distribution method based on brake noise prediction further comprises the following steps: stopping adjusting the brake energy distribution ratio when the vehicle anti-lock braking system is detected to start.

[0013] On the other hand, an embodiment of the present application provides a brake energy distribution system based on brake noise prediction, comprising: a data acquisition module for acquiring brake noise time series data, vehicle speed time series data, hydraulic brake torque time series data and pedal stroke time series data of a current vehicle; a noise prediction module for inputting the brake noise time series data, the hydraulic brake torque time series data and the pedal stroke time series data into a pre-trained brake noise prediction model to obtain a brake noise prediction value of the current vehicle after a preset time step; a noise threshold value determination module for determining a corresponding brake noise threshold value according to a current working condition and a current driving mode of the vehicle; a brake energy adjustment module for adjusting a brake energy distribution ratio so that the energy recovery torque increases and the hydraulic brake torque decreases when the brake noise prediction value is greater than the brake noise threshold value.

[0014] In another aspect, an electronic device is provided, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection communication between the processor and the memory, and the program, when executed by the processor, realizes the brake energy distribution method based on brake noise prediction as described above.

[0015] In another aspect, a vehicle is also provided, which includes the brake energy distribution system based on brake noise prediction or the electronic device as described above.

[0016] In another aspect, a storage medium is also provided, which is a computer-readable storage medium for computer-readable storage, and stores one or more programs executable by one or more processors to realize the brake energy distribution method based on brake noise prediction as described above.

[0017] In another aspect, a computer program product is also provided, which includes a computer program executable by a processor to realize the brake energy distribution method based on brake noise prediction as described above.

[0018] The advantages and beneficial effects of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application: The embodiment of the present application obtains brake noise time series data, vehicle speed time series data, hydraulic brake torque time series data and pedal stroke time series data of a current vehicle, inputs the brake noise time series data, the hydraulic brake torque time series data and the pedal stroke time series data into a brake noise prediction model pre-trained, obtains a brake noise prediction value of the current vehicle after a preset time step, determines a corresponding brake noise threshold value according to a current working condition and a current driving mode of the current vehicle, adjusts a brake energy distribution ratio when the brake noise prediction value is greater than the brake noise threshold value, so that the energy recovery torque increases and the hydraulic brake torque decreases. The embodiment of the present application can quickly adjust the ratio of the hydraulic brake torque and the energy recovery torque when the vehicle brake noise is about to exceed the threshold value without increasing the hardware cost and the material cost, by predicting the brake noise and dynamically distributing the brake energy, so that the vehicle brake noise is maintained below the threshold value, avoiding interference to the user in the vehicle, and improving the driving comfort of the vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following introduces the drawings needed to be used in the embodiments of the present application. It should be understood that the drawings introduced in the following are only for facilitating the clear description of the technical solutions in the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without paying any creative labor.

[0020] Figure 1 A step flow chart of the brake energy distribution method based on brake noise prediction provided by the embodiment of the present application is shown in the following. Figure 2 An implementation scenario diagram of the brake energy distribution method based on brake noise prediction provided by the embodiment of the present application is shown in the following. Figure 3 A structure diagram of the brake energy distribution system based on brake noise prediction provided by the embodiment of the present application is shown in the following. Figure 4 A hardware structure diagram of the electronic device provided by the embodiment of the present application is shown in the following. DETAILED DESCRIPTION

[0021] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the drawings, wherein the same or similar notations represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application. It should be noted that although the functional modules are divided in the system diagram, and the logical order is shown in the flow chart, in some cases, the steps shown or described can be executed in a different order from the module division in the system diagram or the order in the flow chart. For the step numbers in the following embodiments, they are only set for the convenience of description, and the order between the steps is not limited in any way, and the execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0022] In the description of the present application, the meaning of multiple is two or more than two, and if the first and the second are described, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the sequence of indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application, and are not intended to limit the present application.

[0023] It should be noted that in various specific embodiments of the present application, when relevant processing needs to be performed on data related to the identity or characteristics of the user, such as user information, user behavior data, user history data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards of the relevant country or region. In addition, when the embodiments of the present application need to obtain sensitive personal information of the user, the user's separate permission or separate consent will be obtained through a pop-up window or by jumping to a confirmation page, and after obtaining the user's separate permission or separate consent, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0024] As Figure 1 shown is a step flowchart of a brake energy distribution method based on brake noise prediction provided by an embodiment of the present application, referring to Figure 1 , an embodiment of the present application provides a brake energy distribution method based on brake noise prediction, specifically comprising the following steps: S11, obtaining brake noise time series data, vehicle speed time series data, hydraulic brake torque time series data, and pedal stroke time series data of a current vehicle; S12, inputting the brake noise time series data, the hydraulic brake torque time series data, and the pedal stroke time series data into a brake noise prediction model pre-trained to obtain a brake noise prediction value of the current vehicle after a preset time step; S13, determining a corresponding brake noise threshold value according to the current working condition and the current driving mode of the current vehicle; S14, when the brake noise prediction value is greater than the brake noise threshold value, adjusting the brake energy distribution ratio so that the energy recovery torque increases and the hydraulic brake torque decreases.

[0025] Specifically, the microphone sensor is installed on the caliper or the brake, the brake noise time series data generated during the braking of the vehicle is monitored in real time, the brake noise prediction model is input with the real-time collected vehicle speed time series data, hydraulic brake torque time series data, and pedal stroke time series data to obtain brake noise prediction values after a plurality of time steps; after being collected through real vehicle test and evaluated by NVH, the thresholds of brake noise under different working conditions and different driving modes are obtained, the corresponding brake noise threshold value is determined according to the current working condition and the current driving mode of the current vehicle; when the brake noise prediction value is greater than the brake noise threshold value, the energy recovery torque is increased and the hydraulic brake torque is decreased by adjusting the brake energy distribution ratio, so that the vehicle brake noise is maintained below the threshold value, not only solving the problem of brake noise affecting the driving experience, but also reasonably utilizing the brake energy recovery function of the vehicle.

[0026] It can be recognized that the embodiment of the present application can quickly adjust the proportion of the hydraulic braking torque and the energy recovery torque when the vehicle braking noise is about to exceed the threshold value, so that the vehicle braking noise is maintained below the threshold value, the interference to the user in the vehicle is avoided, and the driving comfort of the vehicle is improved, without increasing the hardware cost and material cost.

[0027] Further as an optional implementation, the brake noise prediction model is obtained by the following steps: S21, obtaining historical brake noise time series data, historical vehicle speed time series data, historical hydraulic braking torque time series data and historical pedal stroke time series data of the test vehicle during brake test; S22, selecting the peak value of the historical brake noise time series data as the real noise label, and taking the segment before the peak value in the historical brake noise time series data as the brake noise time series sample; S23, according to the time range of the brake noise time series sample, the historical vehicle speed time series data, the historical hydraulic braking torque time series data and the historical pedal stroke time series data are intercepted to obtain the vehicle speed time series sample, the hydraulic braking torque time series sample and the pedal stroke time series sample; S24, constructing training samples according to the brake noise time series sample, the vehicle speed time series sample, the hydraulic braking torque time series sample and the pedal stroke time series sample, taking the real noise label as the sample label of the training sample, obtaining the training data set; S25, inputting the training data set into the pre-constructed CNN-LSTM hybrid neural network for training to obtain the trained brake noise prediction model.

[0028] Specifically, the historical brake noise time series data, the historical vehicle speed time series data, the historical hydraulic braking torque time series data and the historical pedal stroke time series data of the test vehicle during brake test are obtained, and the time series alignment of these time series data is performed for subsequent processing; the peak value of the historical brake noise time series data is selected as the real noise label, which represents the maximum noise in the brake noise time series data; then the segment before the peak value in the historical brake noise time series data is taken as the brake noise time series sample; according to the time range of the brake noise time series sample, the same time range segments in the historical vehicle speed time series data, the historical hydraulic braking torque time series data and the historical pedal stroke time series data are intercepted to obtain the vehicle speed time series sample, the hydraulic braking torque time series sample and the pedal stroke time series sample; according to the brake noise time series sample, the vehicle speed time series sample, the hydraulic braking torque time series sample and the pedal stroke time series sample, the training samples are constructed, the real noise label is taken as the sample label of the training sample, the training data set is obtained, the training data set is input into the pre-constructed CNN-LSTM hybrid neural network for training, and the trained brake noise prediction model is obtained.

[0029] Further as an optional implementation, the CNN-LSTM hybrid neural network comprises a multi-branch CNN network, a feature fusion layer, an LSTM layer and an output layer, the training data set is input to the pre-constructed CNN-LSTM hybrid neural network for training to obtain a trained brake noise prediction model, which specifically comprises: S251, features of the brake noise time sequence sample, the vehicle speed time sequence sample, the hydraulic brake torque time sequence sample and the pedal stroke time sequence sample are extracted through each branch of the multi-branch CNN network to obtain brake noise features, vehicle speed features, hydraulic brake torque features and pedal stroke features; S252, the brake noise features, the vehicle speed features, the hydraulic brake torque features and the pedal stroke features are fused through the feature fusion layer to obtain a fusion feature vector; S253, the fusion feature vectors of continuous multiple time steps are input to the LSTM layer for hidden state calculation to obtain a hidden state vector; S254, the hidden state vector is mapped to a brake noise prediction label through the output layer; S255, a loss value is determined according to the brake noise prediction label and the real noise label; S256, the parameters of the CNN-LSTM hybrid neural network are updated according to the loss value through a back propagation algorithm to obtain a trained brake noise prediction model.

[0030] Specifically, features of the brake noise time sequence sample, the vehicle speed time sequence sample, the hydraulic brake torque time sequence sample and the pedal stroke time sequence sample are extracted through each branch of the multi-branch CNN network to obtain brake noise features, vehicle speed features, hydraulic brake torque features and pedal stroke features; the features of multiple modalities are fused based on an attention mechanism through the feature fusion layer to obtain a fusion feature vector; the fusion feature vectors of continuous multiple time steps are input to the LSTM layer for hidden state calculation after being composed into time sequence data to capture the time dependence of the time sequence data, and finally a hidden state vector is obtained; the hidden state vector is mapped to a brake noise prediction label through the output layer, a loss value is determined according to the brake noise prediction label and the real noise label, and then the parameters of the CNN-LSTM hybrid neural network are updated according to the loss value through a back propagation algorithm, so that one round of iterative training is completed; when the number of iterations reaches a preset threshold or the loss value is lower than a preset threshold, the training is stopped, and a trained brake noise prediction model is obtained.

[0031] In some embodiments, after the brake noise prediction model is trained, the accuracy of the prediction model can be verified by calibration data, and whether the noise disappears through brake energy distribution can be judged by NVH evaluation.

[0032] Further as optional implementation, the corresponding brake noise threshold value is determined according to the current working condition and the current driving mode of the current vehicle, and the threshold value specifically includes: S131, a brake noise threshold value mapping table calibrated in advance through a real vehicle test is acquired; S132, the brake noise threshold value mapping table is inquired according to the current working condition and the current driving mode, and the brake noise threshold value is obtained.

[0033] Specifically, according to the real vehicle NVH evaluation, the brake noise threshold value under different working conditions and different driving modes is obtained, and it is confirmed that the noise is unacceptable to the driver and the passenger when the brake noise is higher than the threshold value.

[0034] Further as optional implementation, the brake energy distribution ratio is adjusted so that the energy recovery torque increases and the hydraulic brake torque decreases, and the adjustment specifically includes: S141, a brake noise deviation value is determined according to the brake noise prediction value and the brake noise threshold value; S142, a corresponding energy recovery torque compensation value is determined through a PID control algorithm according to the brake noise deviation value; S143, the energy recovery torque is controlled to increase and the hydraulic brake torque is controlled to decrease according to the energy recovery torque compensation value, and the sum of the energy recovery torque and the hydraulic brake torque after the redistribution meets the brake demand of the current vehicle.

[0035] Specifically, the brake noise prediction value is compared with the brake noise threshold value, and when the brake noise prediction value is greater than the brake noise threshold value, it indicates that there is a risk of brake noise affecting the people in the vehicle, at this time, the brake energy distribution ratio is adjusted to stabilize the brake noise below the threshold value until the brake request ends.

[0036] It should be noted that when the brake energy distribution is performed, the premise is that the brake request of the current vehicle must be met; when the wheel energy recovery torque is in the rising or stable state, the hydraulic torque serves as a supplementary torque; when the wheel energy recovery torque is insufficient, the hydraulic torque serves as the main output torque.

[0037] In the embodiment of the application, the brake noise deviation value is determined according to the brake noise prediction value and the brake noise threshold value , the PID controller generates a corresponding energy recovery torque compensation value by calculating the proportional, integral and differential components of the brake noise deviation value in real time , and the discretization formula is:

[0038] wherein, is a proportional gain coefficient, is an integral gain coefficient, is a differential gain coefficient.

[0039] After obtaining the energy recovery torque compensation value, the energy recovery torque is increased based on the energy recovery torque compensation value, and an equivalent amount of hydraulic braking torque can be reduced, thereby reducing the braking noise.

[0040] Further, as an optional embodiment, the brake energy distribution method based on brake noise prediction further comprises the following steps: S15, when the vehicle anti-lock braking system is detected to start, stop adjusting the brake energy distribution ratio.

[0041] Specifically, when the vehicle anti-lock braking system (ABS) starts to work, in order to ensure the stability of the vehicle, the brake noise monitoring and adjusting brake energy distribution system is suspended.

[0042] The method steps of the embodiments of the application are described above. The application will be further described below in combination with a specific embodiment.

[0043] As Figure 2 The figure shows the implementation scene of the brake energy distribution method based on brake noise prediction provided by the embodiment of the application, which includes the right front wheel 100, the left front wheel 103, the right rear wheel 106, the left rear wheel 109, the right front microphone sensor 102, the left front microphone sensor 105, the right rear microphone sensor 108, the left rear microphone sensor 111, the right front brake 101, the left front brake 104, the right rear brake 107, the left rear brake 110, and the integrated brake control system 112. Each wheel is equipped with a microphone sensor, which monitors the noise emitted by the wheel during braking at all times. The four microphone sensors (102, 105, 108, 111) send the brake noise signals read to the integrated brake control system 112. The integrated brake control system 112 combines the signals transmitted by the four microphone sensors (102, 105, 108, 111) with the vehicle speed time sequence data, the hydraulic braking torque time sequence data, and the pedal stroke time sequence data to make a prediction through a model. If it is predicted that the brake noise value of a certain wheel under the corresponding working condition may have a trend of being greater than the threshold value, it is determined that the driver will have unacceptable brake noise for the wheel, and the integrated brake control system 112 will send a control signal to adjust the brake energy distribution to keep the brake noise below the threshold value under the condition of meeting the brake requirements. The difference from the threshold value can be defined through real vehicle calibration or functional specification.

[0044] For example, when the vehicle is running braking, in the case that the vehicle speed is higher than a preset threshold and an anti-lock braking system (ABS) is not working, four microphone sensors (102, 105, 108, 111) monitor the braking noise decibel in this working condition and form a signal to the integrated brake control system 112, and the integrated brake control system 112 also monitors the vehicle speed, pedal stroke, braking torque and other information corresponding to the monitored braking noise. It is predicted by the model that the right front brake 101 at the right front wheel 100 will generate braking noise higher than the threshold value, and then the braking energy distribution of the right front brake 101 is carried out, the proportional relationship between the hydraulic torque and the energy recovery torque is adjusted, so that the braking noise is always below the threshold value until the braking request ends, and the braking energy distribution mode exits. If the rear wheel braking pressure is too large, the anti-lock braking system (ABS) starts to work, and the braking noise monitoring and adjusting braking energy distribution system suspends work.

[0045] It can be understood that, without increasing the hardware cost and material cost, the embodiment of the application can quickly adjust the proportion of the hydraulic braking torque and the energy recovery torque when the vehicle braking noise is about to exceed the threshold value, so that the vehicle braking noise is maintained below the threshold value, the interference to the user in the vehicle is avoided, and the driving comfort of the vehicle is improved.

[0046] As Figure 3 The structure of the braking energy distribution system based on braking noise prediction provided by the embodiment of the application is shown in the figure, and Figure 3 The embodiment of the application provides a braking energy distribution system based on braking noise prediction, which comprises: A data acquisition module is configured to acquire braking noise time series data, vehicle speed time series data, hydraulic braking torque time series data and pedal stroke time series data of a current vehicle. A noise prediction module is configured to input the braking noise time series data, the hydraulic braking torque time series data and the pedal stroke time series data into a pre-trained braking noise prediction model to obtain a braking noise prediction value of the current vehicle after a preset time step. A noise threshold value determination module is configured to determine a corresponding braking noise threshold value according to a current working condition and a current driving mode of the current vehicle. A braking energy adjustment module is configured to adjust a braking energy distribution ratio when the braking noise prediction value is greater than the braking noise threshold value, so that the energy recovery torque is increased and the hydraulic braking torque is reduced.

[0047] It can be understood that the contents in the above method embodiments are applicable to the system embodiment, the functions realized by the device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved by the device embodiment are also the same as those of the above method embodiments.

[0048] The embodiment of the present application further provides an electronic device, which comprises a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection communication between the processor and the memory, and the program is executed by the processor to realize the brake energy distribution method based on brake noise prediction. The electronic device can be any intelligent terminal, such as a tablet computer or a vehicle-mounted computer.

[0049] As shown in Figure 4 Fig. 1 is a schematic diagram of a hardware structure of an electronic device provided by an embodiment of the present application. As shown in Figure 4 The embodiment of the present application provides an electronic device, which comprises: The processor 401 can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to realize the technical solutions provided by the embodiment of the present application. The memory 402 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory). The memory 402 can store an operating system and other application programs. When the technical solutions provided by the embodiment of the present application are implemented by software or firmware, the related program codes are stored in the memory 402 and are called and executed by the processor 401 to realize the brake energy distribution method based on brake noise prediction. The input / output interface 403 is used to realize information input and output. The communication interface 404 is used to realize communication interaction between the device and other devices. The communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.). The bus 405 is used to transmit information between various components (for example, the processor 401, the memory 402, the input / output interface 403, and the communication interface 404) of the device. The processor 401, the memory 402, the input / output interface 403, and the communication interface 404 are connected with each other through the bus 405 to realize communication connection within the device.

[0050] It can be understood that the contents in the above method embodiments are all applicable to the present device embodiments, the present device embodiments specifically implement the functions same as those of the above method embodiments, and achieve the same beneficial effects as those of the above method embodiments.

[0051] The present application also provides a vehicle comprising the above brake energy distribution system or electronic device based on brake noise prediction or the electric drive assembly.

[0052] The vehicle can be a private car, such as a sedan, an SUV, an MPV, or a pickup truck, etc. The vehicle can also be an operating vehicle, such as a van, a bus, a small truck, or a large trailer, etc. The vehicle needs to have an electric motor that can output power or store mechanical energy as a generator. When the vehicle is a new energy vehicle, it can be a hybrid vehicle or a pure electric vehicle.

[0053] Since the vehicle applies all the technical solutions of the above brake energy distribution system or electronic device based on brake noise prediction, it at least has all the beneficial effects brought by the technical solutions of the above embodiments, which will not be repeated here.

[0054] The present application also provides a storage medium, which is a computer-readable storage medium for computer-readable storage, and stores one or more programs that can be executed by one or more processors to implement the above brake energy distribution method based on brake noise prediction.

[0055] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0056] It can be understood that the contents in the above method embodiments are all applicable to the present storage medium embodiments, the present storage medium embodiments specifically implement the functions same as those of the above method embodiments, and achieve the same beneficial effects as those of the above method embodiments.

[0057] The embodiment of the present application further provides a computer program product or computer program, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device can read the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method shown in the embodiment of the present application. Figure 1 The embodiment of the present application further provides a computer program product or computer program, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device can read the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method shown in the embodiment of the present application.

[0058] It can be understood that the contents in the above method embodiments are all applicable to the present program product embodiment, the function specifically implemented by the present program product embodiment is the same as that of the above method embodiments, and the beneficial effects achieved by the present program product embodiment are also the same as those achieved by the above method embodiments.

[0059] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. It can be known by those skilled in the art that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0060] The terms "first", "second", "third", "fourth" and the like (if any) in the description of the present application and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0061] In some alternative embodiments, the functions / operations mentioned in the block diagram can not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two blocks shown in succession can actually be executed substantially simultaneously or the above blocks can be executed in reverse order at times. In addition, the embodiments presented and described in the flowcharts of the present application are provided by way of example, and the purpose is to provide a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are independently executed.

[0062] Furthermore, although the present application is described in the context of functional modules, it is to be understood that one or more of the functions and / or features described above can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It will also be appreciated that detailed discussion of the actual implementation of each module is unnecessary to an understanding of the present application. Rather, the actual implementation of the modules, in conjunction with their attributes, functions, and internal relationships, are to be understood within the context of the devices disclosed herein. Thus, those skilled in the art with access to the teachings presented herein will be able to devise suitable implementations of the present application without undue experimentation. It is also to be understood that the particular concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is defined by the appended claims and equivalents thereof.

[0063] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described above in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0064] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a list of executable instructions for implementing logic functions, which can be specifically embodied in any computer readable medium for use by an instruction execution system, device or apparatus, such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from the instruction execution system, device or apparatus, or in conjunction with these instructions execution system, device or apparatus. For the purpose of this specification, "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by an instruction execution system, device or apparatus, or in conjunction with these instruction execution system, device or apparatus.

[0065] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0066] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware which are stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0067] In the above description of the present specification, the description referring to the terms "one embodiment", "another embodiment", or "certain embodiments" or the like means that a specific feature, structure, material or characteristic described in connection with the embodiments or examples is included in at least one embodiment or example of the present application. The illustrative expressions of the above terms do not necessarily refer to the same embodiment or example in the present specification. Also, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0068] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, alternatives and variations to these embodiments can be made without departing from the principles and spirit of the application, the scope of which is defined in the appended claims and their equivalents.

[0069] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the above-described embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and these equivalent modifications or substitutions are included in the scope defined by the claims of the present application.

Claims

1. A braking energy distribution method based on braking noise prediction, characterized in that, Includes the following steps: Acquire the current vehicle's braking noise timing data, vehicle speed timing data, hydraulic braking torque timing data, and pedal travel timing data; The braking noise timing data, the hydraulic braking torque timing data, and the pedal travel timing data are input into a pre-trained braking noise prediction model to obtain the braking noise prediction value of the current vehicle after a preset time step. The corresponding braking noise threshold value is determined based on the current operating conditions and driving mode of the current vehicle. When the predicted braking noise value is greater than the braking noise threshold value, the braking energy distribution ratio is adjusted so that the energy recovery torque increases and the hydraulic braking torque decreases.

2. The braking energy distribution method based on braking noise prediction according to claim 1, characterized in that, The brake noise prediction model is trained through the following steps: Acquire historical braking noise time-series data, historical vehicle speed time-series data, historical hydraulic braking torque time-series data, and historical pedal travel time-series data of the test vehicle during braking tests; The peak value of the historical braking noise time series data is selected as the real noise label, and the segment of the historical braking noise time series data located before the peak value is used as the braking noise time series sample. Based on the time range of the braking noise timing sample, the historical vehicle speed timing data, the historical hydraulic braking torque timing data, and the historical pedal travel timing data are extracted to obtain vehicle speed timing samples, hydraulic braking torque timing samples, and pedal travel timing samples. Training samples are constructed based on the braking noise time-series samples, the vehicle speed time-series samples, the hydraulic braking torque time-series samples, and the pedal travel time-series samples. The real noise labels are used as the sample labels of the training samples to obtain the training dataset. The training dataset is input into a pre-built CNN-LSTM hybrid neural network for training to obtain the trained brake noise prediction model.

3. The braking energy distribution method based on braking noise prediction according to claim 2, characterized in that, The CNN-LSTM hybrid neural network includes a multi-branch CNN network, a feature fusion layer, an LSTM layer, and an output layer. The step of inputting the training dataset into the pre-constructed CNN-LSTM hybrid neural network for training to obtain the trained braking noise prediction model specifically includes: The braking noise time series sample, the vehicle speed time series sample, the hydraulic braking torque time series sample, and the pedal travel time series sample are respectively extracted by each branch of the multi-branch CNN network to obtain braking noise features, vehicle speed features, hydraulic braking torque features, and pedal travel features. The feature fusion layer performs feature fusion on the brake noise feature, the vehicle speed feature, the hydraulic braking torque feature, and the pedal travel feature to obtain a fused feature vector; The fused feature vectors from multiple consecutive time steps are input into the LSTM layer to calculate the hidden state, thereby obtaining the hidden state vector. The hidden state vector is mapped to a brake noise prediction label through the output layer; The loss value is determined based on the brake noise prediction label and the actual noise label; The parameters of the CNN-LSTM hybrid neural network are updated using the backpropagation algorithm based on the loss value to obtain the trained braking noise prediction model.

4. The braking energy distribution method based on braking noise prediction according to claim 1, characterized in that, The step of determining the corresponding braking noise threshold value based on the current operating conditions and driving mode of the current vehicle specifically includes: Obtain a pre-calibrated braking noise threshold value mapping table based on real vehicle tests; The brake noise threshold value is obtained by querying the brake noise threshold value mapping table based on the current operating conditions and the current driving mode.

5. The braking energy distribution method based on braking noise prediction according to claim 1, characterized in that, The adjustment of the braking energy distribution ratio, which increases the energy recovery torque and decreases the hydraulic braking torque, specifically includes: The brake noise deviation value is determined based on the predicted brake noise value and the brake noise threshold value. The corresponding energy recovery torque compensation value is determined by a PID control algorithm based on the braking noise deviation value. The energy recovery torque is increased and the hydraulic braking torque is decreased according to the energy recovery torque compensation value, so that the sum of the redistributed energy recovery torque and the hydraulic braking torque meets the braking requirements of the current vehicle.

6. A braking energy distribution method based on braking noise prediction according to any one of claims 1 to 5, characterized in that, The braking energy distribution method based on braking noise prediction further includes the following steps: When the vehicle's anti-lock braking system is detected to be activated, the adjustment of the braking energy distribution ratio is stopped.

7. A braking energy distribution system based on braking noise prediction, characterized in that, include: The data acquisition module is used to acquire the current vehicle's braking noise timing data, vehicle speed timing data, hydraulic braking torque timing data, and pedal travel timing data. The noise prediction module is used to input the brake noise timing data, the hydraulic braking torque timing data, and the pedal travel timing data into a pre-trained brake noise prediction model to obtain the brake noise prediction value of the current vehicle after a preset time step. The noise threshold determination module is used to determine the corresponding braking noise threshold value based on the current operating conditions and current driving mode of the current vehicle. The braking energy adjustment module is used to adjust the braking energy distribution ratio when the predicted braking noise value is greater than the braking noise threshold value, so as to increase the energy recovery torque and decrease the hydraulic braking torque.

8. An electronic device, characterized in that, The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, it implements the braking energy distribution method based on braking noise prediction as described in any one of claims 1 to 6.

9. A vehicle, characterized in that, The vehicle includes the brake energy distribution system based on brake noise prediction as described in claim 7 or the electronic device as described in claim 8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the braking energy distribution method based on braking noise prediction as described in any one of claims 1 to 6.

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